Optimal parameter identification strategy applied to lithium-ion battery model for electric vehicles using drive cycle data

The optimal parameter identification of lithium-ion (Li-ion) battery models is essential for accurately capturing battery behavior and performance in electric vehicle (EV) applications. Traditional methods for parameter identification often rely on manual tuning or trial-and-error approaches, which can be time-consuming and yield suboptimal results. In recent years, metaheuristic optimization algorithms have emerged as powerful tools for efficiently searching and identifying optimal parameter values. This paper proposes an optimal parameter identification strategy using a metaheuristic optimization algorithm applied to a Shepherd model for EV applications. The identification technique that was based on the Self-adaptive Bonobo Optimizer (SaBO) performed extremely well when it came to the process of identifying the battery's unidentified properties. Because of this, the overall voltage error of the suggested identification technique has been lowered to 4.2377 × 10⁻³, and the root mean square error (RMSE) between the model and the data has been calculated to be 8.64 × 10⁻³. In addition, compared to the other optimization methods, the optimization efficiency was able to attain 96.6%, which validated its efficiency.